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<li><a class="reference internal" href="#">Comparing different clustering algorithms on toy datasets</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="comparing-different-clustering-algorithms-on-toy-datasets">
<span id="sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"></span><h1>Comparing different clustering algorithms on toy datasets<a class="headerlink" href="#comparing-different-clustering-algorithms-on-toy-datasets" title="Permalink to this headline">¶</a></h1>
<p>This example shows characteristics of different
clustering algorithms on datasets that are “interesting”
but still in 2D. With the exception of the last dataset,
the parameters of each of these dataset-algorithm pairs
has been tuned to produce good clustering results. Some
algorithms are more sensitive to parameter values than
others.</p>
<p>The last dataset is an example of a ‘null’ situation for
clustering: the data is homogeneous, and there is no good
clustering. For this example, the null dataset uses the
same parameters as the dataset in the row above it, which
represents a mismatch in the parameter values and the
data structure.</p>
<p>While these examples give some intuition about the
algorithms, this intuition might not apply to very high
dimensional data.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">warnings</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">mixture</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">kneighbors_graph</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">cycle</span><span class="p">,</span> <span class="n">islice</span>

<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

<span class="c1"># ============</span>
<span class="c1"># Generate datasets. We choose the size big enough to see the scalability</span>
<span class="c1"># of the algorithms, but not too big to avoid too long running times</span>
<span class="c1"># ============</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1500</span>
<span class="n">noisy_circles</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_circles</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">factor</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span>
                                      <span class="n">noise</span><span class="o">=.</span><span class="mi">05</span><span class="p">)</span>
<span class="n">noisy_moons</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_moons</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">noise</span><span class="o">=.</span><span class="mi">05</span><span class="p">)</span>
<span class="n">blobs</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">no_structure</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="kc">None</span>

<span class="c1"># Anisotropicly distributed data</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="mi">170</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
<span class="n">transformation</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.6</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]]</span>
<span class="n">X_aniso</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">transformation</span><span class="p">)</span>
<span class="n">aniso</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_aniso</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

<span class="c1"># blobs with varied variances</span>
<span class="n">varied</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span>
                             <span class="n">cluster_std</span><span class="o">=</span><span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span>
                             <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>

<span class="c1"># ============</span>
<span class="c1"># Set up cluster parameters</span>
<span class="c1"># ============</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">12.5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=.</span><span class="mi">02</span><span class="p">,</span> <span class="n">right</span><span class="o">=.</span><span class="mi">98</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=.</span><span class="mi">001</span><span class="p">,</span> <span class="n">top</span><span class="o">=.</span><span class="mi">96</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span>
                    <span class="n">hspace</span><span class="o">=.</span><span class="mi">01</span><span class="p">)</span>

<span class="n">plot_num</span> <span class="o">=</span> <span class="mi">1</span>

<span class="n">default_base</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;quantile&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span>
                <span class="s1">&#39;eps&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span>
                <span class="s1">&#39;damping&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">9</span><span class="p">,</span>
                <span class="s1">&#39;preference&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">200</span><span class="p">,</span>
                <span class="s1">&#39;n_neighbors&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
                <span class="s1">&#39;n_clusters&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
                <span class="s1">&#39;min_samples&#39;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span>
                <span class="s1">&#39;xi&#39;</span><span class="p">:</span> <span class="mf">0.05</span><span class="p">,</span>
                <span class="s1">&#39;min_cluster_size&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">}</span>

<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span>
    <span class="p">(</span><span class="n">noisy_circles</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;damping&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">77</span><span class="p">,</span> <span class="s1">&#39;preference&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">240</span><span class="p">,</span>
                     <span class="s1">&#39;quantile&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;n_clusters&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
                     <span class="s1">&#39;min_samples&#39;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span> <span class="s1">&#39;xi&#39;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">}),</span>
    <span class="p">(</span><span class="n">noisy_moons</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;damping&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">75</span><span class="p">,</span> <span class="s1">&#39;preference&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">220</span><span class="p">,</span> <span class="s1">&#39;n_clusters&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">}),</span>
    <span class="p">(</span><span class="n">varied</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;eps&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">18</span><span class="p">,</span> <span class="s1">&#39;n_neighbors&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
              <span class="s1">&#39;min_samples&#39;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;xi&#39;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span> <span class="s1">&#39;min_cluster_size&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">2</span><span class="p">}),</span>
    <span class="p">(</span><span class="n">aniso</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;eps&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">15</span><span class="p">,</span> <span class="s1">&#39;n_neighbors&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
             <span class="s1">&#39;min_samples&#39;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span> <span class="s1">&#39;xi&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span> <span class="s1">&#39;min_cluster_size&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">2</span><span class="p">}),</span>
    <span class="p">(</span><span class="n">blobs</span><span class="p">,</span> <span class="p">{}),</span>
    <span class="p">(</span><span class="n">no_structure</span><span class="p">,</span> <span class="p">{})]</span>

<span class="k">for</span> <span class="n">i_dataset</span><span class="p">,</span> <span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">algo_params</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
    <span class="c1"># update parameters with dataset-specific values</span>
    <span class="n">params</span> <span class="o">=</span> <span class="n">default_base</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
    <span class="n">params</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">algo_params</span><span class="p">)</span>

    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">dataset</span>

    <span class="c1"># normalize dataset for easier parameter selection</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>

    <span class="c1"># estimate bandwidth for mean shift</span>
    <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">estimate_bandwidth</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;quantile&#39;</span><span class="p">])</span>

    <span class="c1"># connectivity matrix for structured Ward</span>
    <span class="n">connectivity</span> <span class="o">=</span> <span class="n">kneighbors_graph</span><span class="p">(</span>
        <span class="n">X</span><span class="p">,</span> <span class="n">n_neighbors</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_neighbors&#39;</span><span class="p">],</span> <span class="n">include_self</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="c1"># make connectivity symmetric</span>
    <span class="n">connectivity</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">connectivity</span> <span class="o">+</span> <span class="n">connectivity</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>

    <span class="c1"># ============</span>
    <span class="c1"># Create cluster objects</span>
    <span class="c1"># ============</span>
    <span class="n">ms</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">MeanShift</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">bin_seeding</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">two_means</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">MiniBatchKMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_clusters&#39;</span><span class="p">])</span>
    <span class="n">ward</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">AgglomerativeClustering</span><span class="p">(</span>
        <span class="n">n_clusters</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_clusters&#39;</span><span class="p">],</span> <span class="n">linkage</span><span class="o">=</span><span class="s1">&#39;ward&#39;</span><span class="p">,</span>
        <span class="n">connectivity</span><span class="o">=</span><span class="n">connectivity</span><span class="p">)</span>
    <span class="n">spectral</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">SpectralClustering</span><span class="p">(</span>
        <span class="n">n_clusters</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_clusters&#39;</span><span class="p">],</span> <span class="n">eigen_solver</span><span class="o">=</span><span class="s1">&#39;arpack&#39;</span><span class="p">,</span>
        <span class="n">affinity</span><span class="o">=</span><span class="s2">&quot;nearest_neighbors&quot;</span><span class="p">)</span>
    <span class="n">dbscan</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;eps&#39;</span><span class="p">])</span>
    <span class="n">optics</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">OPTICS</span><span class="p">(</span><span class="n">min_samples</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;min_samples&#39;</span><span class="p">],</span>
                            <span class="n">xi</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;xi&#39;</span><span class="p">],</span>
                            <span class="n">min_cluster_size</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;min_cluster_size&#39;</span><span class="p">])</span>
    <span class="n">affinity_propagation</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">AffinityPropagation</span><span class="p">(</span>
        <span class="n">damping</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;damping&#39;</span><span class="p">],</span> <span class="n">preference</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;preference&#39;</span><span class="p">])</span>
    <span class="n">average_linkage</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">AgglomerativeClustering</span><span class="p">(</span>
        <span class="n">linkage</span><span class="o">=</span><span class="s2">&quot;average&quot;</span><span class="p">,</span> <span class="n">affinity</span><span class="o">=</span><span class="s2">&quot;cityblock&quot;</span><span class="p">,</span>
        <span class="n">n_clusters</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_clusters&#39;</span><span class="p">],</span> <span class="n">connectivity</span><span class="o">=</span><span class="n">connectivity</span><span class="p">)</span>
    <span class="n">birch</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">Birch</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_clusters&#39;</span><span class="p">])</span>
    <span class="n">gmm</span> <span class="o">=</span> <span class="n">mixture</span><span class="o">.</span><span class="n">GaussianMixture</span><span class="p">(</span>
        <span class="n">n_components</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;n_clusters&#39;</span><span class="p">],</span> <span class="n">covariance_type</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">)</span>

    <span class="n">clustering_algorithms</span> <span class="o">=</span> <span class="p">(</span>
        <span class="p">(</span><span class="s1">&#39;MiniBatchKMeans&#39;</span><span class="p">,</span> <span class="n">two_means</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;AffinityPropagation&#39;</span><span class="p">,</span> <span class="n">affinity_propagation</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;MeanShift&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;SpectralClustering&#39;</span><span class="p">,</span> <span class="n">spectral</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;Ward&#39;</span><span class="p">,</span> <span class="n">ward</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;AgglomerativeClustering&#39;</span><span class="p">,</span> <span class="n">average_linkage</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;DBSCAN&#39;</span><span class="p">,</span> <span class="n">dbscan</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;OPTICS&#39;</span><span class="p">,</span> <span class="n">optics</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;Birch&#39;</span><span class="p">,</span> <span class="n">birch</span><span class="p">),</span>
        <span class="p">(</span><span class="s1">&#39;GaussianMixture&#39;</span><span class="p">,</span> <span class="n">gmm</span><span class="p">)</span>
    <span class="p">)</span>

    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">algorithm</span> <span class="ow">in</span> <span class="n">clustering_algorithms</span><span class="p">:</span>
        <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

        <span class="c1"># catch warnings related to kneighbors_graph</span>
        <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
                <span class="s2">&quot;ignore&quot;</span><span class="p">,</span>
                <span class="n">message</span><span class="o">=</span><span class="s2">&quot;the number of connected components of the &quot;</span> <span class="o">+</span>
                <span class="s2">&quot;connectivity matrix is [0-9]{1,2}&quot;</span> <span class="o">+</span>
                <span class="s2">&quot; &gt; 1. Completing it to avoid stopping the tree early.&quot;</span><span class="p">,</span>
                <span class="n">category</span><span class="o">=</span><span class="ne">UserWarning</span><span class="p">)</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
                <span class="s2">&quot;ignore&quot;</span><span class="p">,</span>
                <span class="n">message</span><span class="o">=</span><span class="s2">&quot;Graph is not fully connected, spectral embedding&quot;</span> <span class="o">+</span>
                <span class="s2">&quot; may not work as expected.&quot;</span><span class="p">,</span>
                <span class="n">category</span><span class="o">=</span><span class="ne">UserWarning</span><span class="p">)</span>
            <span class="n">algorithm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>

        <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">algorithm</span><span class="p">,</span> <span class="s1">&#39;labels_&#39;</span><span class="p">):</span>
            <span class="n">y_pred</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">labels_</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">y_pred</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>

        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">clustering_algorithms</span><span class="p">),</span> <span class="n">plot_num</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">i_dataset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>

        <span class="n">colors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">islice</span><span class="p">(</span><span class="n">cycle</span><span class="p">([</span><span class="s1">&#39;#377eb8&#39;</span><span class="p">,</span> <span class="s1">&#39;#ff7f00&#39;</span><span class="p">,</span> <span class="s1">&#39;#4daf4a&#39;</span><span class="p">,</span>
                                             <span class="s1">&#39;#f781bf&#39;</span><span class="p">,</span> <span class="s1">&#39;#a65628&#39;</span><span class="p">,</span> <span class="s1">&#39;#984ea3&#39;</span><span class="p">,</span>
                                             <span class="s1">&#39;#999999&#39;</span><span class="p">,</span> <span class="s1">&#39;#e41a1c&#39;</span><span class="p">,</span> <span class="s1">&#39;#dede00&#39;</span><span class="p">]),</span>
                                      <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))))</span>
        <span class="c1"># add black color for outliers (if any)</span>
        <span class="n">colors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">colors</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;#000000&quot;</span><span class="p">])</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">y_pred</span><span class="p">])</span>

        <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(())</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(())</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">.</span><span class="mi">99</span><span class="p">,</span> <span class="o">.</span><span class="mi">01</span><span class="p">,</span> <span class="p">(</span><span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1">s&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">),</span>
                 <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
                 <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
        <span class="n">plot_num</span> <span class="o">+=</span> <span class="mi">1</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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